How to understand the concept of self-supervised learning in AI?
I don't think your interpretation is correct. Take images as example.


Supervised Learning

e.g. classification (maybe use CNN with a L2 loss function)

Assume you have many images with different labels. You wish to find a function to approximate the function $y=f(x)$ given a lot of $(\hat x, \hat y)$ sample pairs. 
Unsupervised Learning

e.g. clustering (maybe use k-means) 

Assume you have many images, but we don't have the labels or we just want to see if there's a way to categorize them into different categories. So we cluster the images by some  characteristic that isn't pre-defined.
Self-Supervised Learning

e.g. super resolution (maybe use CNN with a L2 loss function)

You have many high resolution images without labels, but, your target is to train a model to up sample a low resolution image. So you can have the high resolution images as target, and down size the image to be the input, and try to train the image pairs. So the target is not some manually tagged labels, but generated directly from the data.